pfs
A Particle Filtering over Sets Approach to Multi-Object Tracking
This is code release of paper Intention-Aware Multi-Human Tracking for Human-Robot Interaction via Particle Filtering over Sets, Aijun Bai, Reid Simmons, Manuela Veloso, and Xiaoping Chen, AAAI 2014 Fall Symposium: AI for Human-Robot Interaction (AI-HRI), Arlington, Virginia, United States, November 2014.
Experimental results over PETS2009 S2L1 dataset are available at https://www.youtube.com/watch?v=M2VjS2tMNmg.
Generated log files can be played by rcg_player.
Dependencies
- libxml2-dev
- libqt4-dev
- qt4-qmake
- libboost-dev
- libboost-program-options-dev
- libeigen3-dev
- libpopt-dev
Compiling
qmake# Running qmake to generate Makefilemake -j4# Building using Makefile
Running
./PETS-S2L1V1.sh# Running pfs on PETS-S2L1V1 dataset./TUD-Stadtmitte.sh# Running pfs on TUD-Stadtmitte dataset
Options
Allowed options of pfs:
--help Produce help message
--debug arg Debug level
--framerate arg Rate to process depth frames
--test [=arg(=1)] Test by simulation
--task arg Task name
--bag arg Bag files to read from
--runto arg Run to step
--save_input [=arg(=1)] Save all inputs (including time)
--load_input [=arg(=human_tracker.input)]
Load all inputs (including time)
--log_date [=arg(=1)] Use date as log name
--simulator_expected_humans arg Simulator expected number of humans
--simulator_distance_threshold [=arg(=1)]
Simulator statistic distance threshold
--camera_calibration [=arg(=MOT-benchmarks/camera.xml)]
Camera calibration file (for benchmark
testing)
--benchmark_data arg Load benchmark detection and ground
truth data
--cropped [=arg(=1)] Use cropped data
--approximation_test [=arg(=1)] Approximation error test
--interface [=arg(=1)] Show benchmark interface
--side_view [=arg(=1)] Show side-by-side view
--report_threshold arg Report confidence threshold
--seed arg Random seed number
--num_particles arg Number of particles
--position_kernel_size arg Position kernel size
--max_em_steps arg Max EM steps
--approaching_samples arg Human approaching identification
samples
--false_rate arg False detection rate
--missing_rate arg Missing detection rate
--false_density arg False detection density
--false_missing_pruning arg False-missing pruning threshold
--assignments_pruning arg Murty pruning ratio threshold
--option_pruning arg Option pruning ratio threshold
--observation_proposal_prob arg Observation proposal prob
--death_rate arg Death rate
--refinement_rate arg Refinement rate
--human_area_min arg Min human area
--human_area_max arg Max human area
--intention_mode arg Intention initialize mode
--hierarchical_filters [=arg(=1)] Hierarchical particle filters
--mixed_filters [=arg(=1)] Mixed particle filters
--gaussian_approximate [=arg(=1)] Use Gaussian approximate
--assignment_sampling [=arg(=1)] Use assignment sampling
--velocity_augment [=arg(=1)] Use velocity augment
--detection_confidence [=arg(=1)] Use detection confidence
--detection_orientation [=arg(=1)] Use detection orientation
--observation_error arg Observation error
--threads arg Number of threads